class_docstr: A class that computes different kinds of        :term:`functional connectivity` matrices.
  
  .. versionadded:: 0.2
  
  Parameters
  ----------
  cov_estimator : estimator object,                     default=LedoitWolf(store_precision=False)
      The covariance estimator.
      This implies that correlations are slightly shrunk
      towards zero compared to a maximum-likelihood estimate
  
  kind : {"covariance", "correlation", "partial correlation",            "tangent", "precision"}, default='covariance'
      The matrix kind.
      For the use of "tangent" see :footcite:t:`Varoquaux2010b`.
  
  vectorize : bool, default=False
      If True, connectivity matrices are reshaped into 1D arrays and only
      their flattened lower triangular parts are returned.
  
  discard_diagonal : bool, default=False
      If True, vectorized connectivity coefficients do not include the
      matrices diagonal elements. Used only when vectorize is set to True.
  
  %(standardize)s
  
      .. note::
  
          Added to control passing value to `standardize` of ``signal.clean``
          to call new behavior since passing "zscore" or True (default) is
          deprecated. This parameter will be deprecated in version 0.13 and
          removed in version 0.15.
  
  Attributes
  ----------
  cov_estimator_ : estimator object
      A new covariance estimator with the same parameters as cov_estimator.
  
  mean_ : numpy.ndarray
      The mean connectivity matrix across subjects. For 'tangent' kind,
      it is the geometric mean of covariances (a group covariance
      matrix that captures information from both correlation and partial
      correlation matrices). For other values for "kind", it is the
      mean of the corresponding matrices
  
  whitening_ : numpy.ndarray
      The inverted square-rooted geometric mean of the covariance matrices.
  
  References
  ----------
  .. footbibliography::
code: def __init__(
      self,
      cov_estimator=LedoitWolf(store_precision=False),
      kind="covariance",
      vectorize=False,
      discard_diagonal=False,
      standardize=True,
  ):
      self.cov_estimator = cov_estimator
      self.kind = kind
      self.vectorize = vectorize
      self.discard_diagonal = discard_diagonal
      self.standardize = standardize
  
imports: import warnings
  from math import floor, sqrt
  import numpy
  from scipy import linalg
  from sklearn.base import BaseEstimator, TransformerMixin, clone
  from sklearn.covariance import LedoitWolf
  from nilearn._utils.docs import fill_doc
  from ..None import signal
  from .._utils.extmath import is_spd
label_desc: Use image node_id 1 to create a new ConnectivityMeasure object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = ConnectivityMeasure(cov_estimator, kind, vectorize, discard_diagonal, standardize)